Splunk SPLK-5001 - Splunk Certified Cybersecurity Defense Analyst

An event where a user authenticates from geographically distant locations within an impossibly short timeframe is classified as anAccess Anomaly. This type of anomaly reflects unusual or suspicious access behavior that deviates from normal user patterns.

Access Anomaliestypically focus on login behavior, including impossible travel, unusual times, or atypical locations.

Identity Anomaliesrelate to suspicious behavior linked to user identities but are broader than access specifics.

Endpoint Anomaliesfocus on device or host irregularities.

Threat Anomaliesrefer to indications of malicious activity detected by threat intelligence or detection content.

Splunk’sEnterprise Security documentationcategorizes impossible travel as a classic Access Anomaly, frequently flagged by correlation searches and triggering notable events.

Splunk Enterprise Security (ES) provides default annotations for enrichment of correlation searches with common and widely adopted security frameworks, facilitating better contextual understanding and reporting. The default annotation options includeMITRE ATT&CK,CIS Controls, andLockheed Martin Cyber Kill Chainframeworks, which are natively integrated and extensively used in security operations for mapping tactics, techniques, and controls.

OWASP Top 10, while a critical web application security framework, isnot included as a default annotation optionwithin Splunk ES for correlation search enrichment. OWASP Top 10 is often referenced separately in web application security contexts rather than in general SOC threat frameworks.

The inclusion of MITRE ATT&CK and Lockheed Martin Cyber Kill Chain annotations helps analysts categorize attack stages and tactics directly within ES dashboards.

This is confirmed in theSplunk Enterprise Security User Guideand annotations documentation, which lists the default frameworks available for search enrichment.

In Splunk, to filter users with over a thousand occurrences, the pipeline| stats count by user | where count > 1000 | sort - countis most effective. Thestats count by usercommand generates a count of occurrences for each user. Thewhereclause then filters out only those users who have more than 1000 occurrences. Finally,sort - countsorts the results in descending order by count. This approach is efficient for identifying outliers, such as users with a high number of events.

Outlier detection is a fundamental technique in anomaly detection used in cybersecurity and data science. The method identifies data points that deviate significantly from the established norm or clusters of typical behavior. Data points that falloutside high-density clusters(the groups of similar data) are labeled asanomaliesbecause they do not conform to expected patterns.

Anomaliesindicate potentially suspicious or malicious activities, such as unusual login times, unexpected network traffic, or data exfiltration attempts.

Inconsistenciesis a broader term but lacks the precise meaning used in clustering or anomaly detection.

Baselinedrefers to establishing normal behavior patterns rather than identifying deviations.

Non-conformativesis not a standard term in this context.

According to theSplunk Cybersecurity Defense Analyst Study GuideandSplunk Machine Learning Toolkit documentation, detecting anomalies through outlier detection algorithms (e.g., DBSCAN, isolation forest) is key to threat detection and investigation workflows.

Adaptive Response Actions (ARAs)in Splunk Enterprise Security enable analysts to initiate automated or semi-automated remediation workflows directly from ES dashboards or investigations without leaving the interface. When integrated with Splunk SOAR (Security Orchestration, Automation, and Response), ARAs can trigger playbooks designed to execute containment actions on compromised devices and simultaneously update the original investigation or notable event.

Anadaptive response actionis tightly integrated with ES findings and provides immediate feedback into the investigation workflow.

Event-level and field-level workflow actions may support initiating external processes but are less integrated or standardized than ARAs.

Alert actions typically trigger notifications, not complex remediation playbooks.

TheSplunk Enterprise Security documentationhighlights ARAs as a key component for orchestrated response enabling analysts to act decisively within the ES context, ensuring containment activities are both tracked and correlated with initial findings.

Splunk SOAR (Security Orchestration, Automation, and Response) playbooks are designed to automate security tasks, makingtaking containment action on a compromised hostthe best-suited use case. A SOAR playbook can automate the response actions such as isolating a host, blocking IPs, or disabling accounts, based on predefined criteria. This reduces response time and minimizes the impact of security incidents. The other options, like forming hypotheses for threat hunting or visualizing datasets, are more manual processes and less suited for automation via a playbook.

The MITRE ATT&CK framework categorizes Tactics, Techniques, and Procedures (TTPs) used by attackers. It is a globally accessible knowledge base of adversarial tactics and techniques based on real-world observations, and it is widely used by cybersecurity professionals to understand and defend against various cyber threats.

Tactics, Techniques, and Procedures (TTPs):

Tactics:The high-level goals that attackers aim to achieve during an attack. For example, gaining initial access to a network.

Techniques:The specific methods used to achieve these tactics, such as phishing or exploiting a vulnerability.

Procedures:The detailed steps and tools that attackers use to implement a technique.

MITRE ATT&CK Framework:MITRE ATT&CK organizes these TTPs into a matrix that reflects different stages of an attack lifecycle, from initial access to exfiltration. The framework helps security teams by:

Mapping Detection and Defense:Organizations can map their existing detection capabilities against the ATT&CK matrix to identify gaps.

Threat Hunting:Security teams use the framework to guide their threat-hunting activities, focusing on specific techniques associated with known adversaries.

Incident Response:During incident response, analysts can use the ATT&CK framework to understand the behaviors exhibited by an attacker, which helps in creating effective containment and eradication strategies.

Why MITRE ATT&CK:Unlike compliance-focused frameworks like NIST 800-53 or ISO 27000, which provide security controls and best practices, MITRE ATT&CK is specifically focused on the behavior of adversaries. This focus makes it an invaluable resource for understanding how attacks unfold and how to counteract them.

This scenario is an example of aFalse Negativebecause the detection mechanisms failed to generate alerts for a brute-force attack due to a misconfiguration—specifically, the exclusion of Linux data from the detection searches. A False Negative occurs when a security control fails to detect an actual malicious activity that it is supposed to catch, leading to undetected attacks and potential breaches.

The correct Splunk search that returns results in the most performant way isindex=foo host=i-478619733 | stats range(_time) as duration by src_ip | bin duration span=5min | stats count by duration, host. This search is optimized by:

Starting with the most specific search criteria (index and host) to reduce the data set.

Applying aggregation functions (stats) early, which helps minimize the amount of data processed in subsequent commands.

Usingbinto group data efficiently before performing further statistical calculations.

Search Optimization:

Efficient Indexing:By specifyingindex=fooandhost=i-478619733at the start, the search limits the scope of data that needs to be processed, which significantly improves performance.

Early Aggregation:Thestatscommand is used early in the search to aggregate data bysrc_ip, which reduces the volume of data passed to the next stages of the pipeline.

Use ofbin:Grouping durations withbinbefore performing a secondstatsaggregation reduces the number of unique values, making the final stats calculation more efficient.

Performance Considerations:

Order of Operations:Splunk processes search commands from left to right. Starting with a broad data retrieval and then narrowing down with stats and bin commands ensures that the least amount of data is processed in the final stages.

Avoiding Suboptimal Patterns:The other options either apply operations in a less efficient order or involve unnecessary steps that increase processing time and resource usage.